Pandas fillna()

We can use fillna() function from pandas library to fill missing data.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os

def fill_missing_values(df_data):
    df_data.fillna(method='ffill', inplace=True)
    df_data.fillna(method='bfill', inplace=True)

def symbol_to_path(symbol, base_dir="data"):
    return os.path.join(base_dir, "{}.csv".format(str(symbol)))

def get_data(symbols, dates):
    df_final = pd.DataFrame(index=dates)
    if "SPY" not in symbols:  # add SPY for reference, if absent
        symbols.insert(0, "SPY")

    for symbol in symbols:
        file_path = symbol_to_path(symbol)
        df_temp = pd.read_csv(file_path, parse_dates=True, index_col="Date",
            usecols=["Date", "Adj Close"], na_values=["nan"])
        df_temp = df_temp.rename(columns={"Adj Close": symbol})
        df_final = df_final.join(df_temp)
        if symbol == "SPY":  # drop dates SPY did not trade
            df_final = df_final.dropna(subset=["SPY"])

    return df_final

def plot_data(df_data):
    ax = df_data.plot(title="Stock Data", fontsize=2)
    ax.set_xlabel("Date")
    ax.set_ylabel("Price")
    plt.show()

def test_run():
    symbol_list = ["GOOG", "IBM", "XOM"]  # list of symbols
    start_date = "1940-12-31"
    end_date = "2017-12-07"
    dates = pd.date_range(start_date, end_date)  # date range as index
    df_data = get_data(symbol_list, dates)  # get data for each symbol

    # Fill missing values
    fill_missing_values(df_data)

    # Plot
    plot_data(df_data)


if __name__ == "__main__":
    test_run()

Here is the output.